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All Journal Jurnal ULTIMATICS Journal of Soft Computing Exploration
Tumanggor, Gavrila Louise
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Sentiment Analysis in E-Commerce: Beauty Product Reviews Tumanggor, Gavrila Louise; Samosir, Feliks Victor Parningotan
Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3708

Abstract

The increasing popularity of online shopping platforms is fueling the need for automated sentiment analysis for product reviews. This research aims to build an automatic sentiment analysis model in Indonesian for e-commerce product reviews. This model is expected to help consumers make purchasing decisions more quickly. We utilize the IndoBERT model, which has shown to be quite effective for general sentiment analysis, achieving an evaluation accuracy of 66.2% despite a high evaluation loss of 0.8006. The approach used combines Natural Language Processing (NLP) and Machine Learning (ML) techniques. It is hoped that this research will be useful for consumers, shop owners, and researchers in efficiently understanding the sentiment of e-commerce product reviews.
Sentiment Analysis in E-Commerce: Beauty Product Reviews Tumanggor, Gavrila Louise; Samosir, Feliks Victor Parningotan
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3708

Abstract

The increasing popularity of online shopping platforms is fueling the need for automated sentiment analysis for product reviews. This research aims to build an automatic sentiment analysis model in Indonesian for e-commerce product reviews. This model is expected to help consumers make purchasing decisions more quickly. We utilize the IndoBERT model, which has shown to be quite effective for general sentiment analysis, achieving an evaluation accuracy of 66.2% despite a high evaluation loss of 0.8006. The approach used combines Natural Language Processing (NLP) and Machine Learning (ML) techniques. It is hoped that this research will be useful for consumers, shop owners, and researchers in efficiently understanding the sentiment of e-commerce product reviews.
Integrated aspect extraction and sentiment classification for aspect-based sentiment analysis using fine-tuned indoBERT on indonesian e-commerce reviews Samosir, Feliks Victor Parningotan; Tumanggor, Gavrila Louise
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.26

Abstract

The rapid growth of Indonesian e-commerce has generated vast volumes of consumer reviews, yet extracting actionable aspect-level sentiment from informal Indonesian-language texts remains challenging due to the limited availability of domain-specific Aspect-Based Sentiment Analysis (ABSA) models. This study aimed to develop and evaluate an integrated IndoBERT-based ABSA model that combines aspect extraction and aspect sentiment classification within a single framework, applied to Indonesian beauty product reviews. A corpus of 500 beauty product reviews was processed through aspect extraction, yielding approximately 10,000 aspect-level data points labeled as positive or negative. The IndoBERT model was fine-tuned with optimized hyperparameters. The model achieved 86% accuracy, 85.71% F1-score, and 88% balanced accuracy. Aspect-level evaluation revealed F1-scores of 100% for seller, 98% for product, and 86% for shipping. Inference throughput of 33,173 samples per second confirmed real-world deployment feasibility. These results demonstrate the effectiveness of integrated IndoBERT fine-tuning for ABSA on Indonesian e-commerce reviews and provide a foundation for enhancing data-driven marketing strategies in the beauty product sector.
Co-Authors Feliks Victor Parningotan Samosir